VP - Data Architect - Corporate Data Technology

Jefferies is creating a Technology Innovation Hub in Pune, a greenfield opportunity to build the systems that power global markets.

As our first India technology center, this hub brings together hands on builders who engineer the platforms behind Jefferies’ growth across capital markets, investment banking, and institutional securities.

We’re scaling toward an elite team of 500 engineers while maintaining the agility, ownership, and meritocratic spirit that defines Jefferies.

From cloud and data to AI, risk, and core business technologies, teams in Pune will lead high impact work with a global mandate.

Corporate Technology powers Jefferies’ enterprise operations, enabling front-, middle-, and back-office teams to run efficiently, securely, and in compliance at scale.

The group delivers and supports integrated platforms across HR, Finance, Compliance & Legal, Treasury, Client Onboarding, Operations, Centralized Data Services, Regulatory Reporting, Loan Operations, and FX Trading, while also leading firmwide digitalization initiatives focused on automation and process efficiency.

The team uses modern technology stack—including AWS and Azure, Snowflake, Qlik Sense, and best-in-class vendor solutions—to build next-generation capabilities in AI/ML, enterprise analytics, and process automation (RPA and low-code/no-code).

Roles in Corporate Technology offer hands-on work with cloud, data, and automation platforms, close collaboration with engineers, product managers, and business stakeholders, and end-to-end exposure to how a global financial services firm operates

We are seeking a skilled and detail-oriented Senior Data Architect to join our cross-functional Agile team.

In this role, you will be responsible for designing, building, and maintaining the overall structure of cloud data platforms using Snowflake.

Define how data is stored, integrated, secured, and used across the organization, making sure the system is scalable, high-performing, and aligned with business goals.

You will collaborate closely with backend and frontend engineers, product managers, and analysts to create analytics-ready data models that power application features, reporting, and strategic insights.

This is an exciting opportunity for someone who values clean data design, modern tooling, and working at the intersection of engineering and business.

• Design enterprise-level data architecture using Snowflake
• Define data models, schemas, and standards (star, snowflake, data vault)
• Architect data ingestion and transformation strategies (ETL/ELT)
• Ensure scalability, performance, and cost optimization in Snowflake
• Design data security and governance (roles, policies, masking, compliance)
• Choose and integrate tools (dbt, Airflow, Kafka, etc.)
• Guide data engineers and review technical designs
• Establish best practices and architectural documentation
• Collaborate with business, analytics, and leadership teams
• Plan cloud architecture across AWS

• Deep expertise in Snowflake architecture
• Experience with dbt, Airflow, Dagster or streaming tools
• Knowledge of Data Vault 2.0
• Experience designing enterprise data platforms
• Experience with BI tools (Sigma, Qlik)
• Advanced SQL and data modeling
• Strong knowledge of cloud platforms
• Experience with ETL/ELT frameworks
• Data warehousing and analytics architecture
• Understanding of performance tuning and cost control
• Familiarity with data governance, security, and compliance
• System-level thinking and Strategic problem-solver
• Leadership and mentoring ability
• Strong communication and collaboration skills, with the ability to work across engineering and business teams.
• Experience working in Agile/Scrum environments and managing work using Jira.

• Bachelor’s or master’s degree in computer science, Data Engineering, or related field
• Typically, 7–10+ years’ experience in data engineering or analytics
• Proven experience in architecture or lead role
• Snowflake and cloud certifications
• A track record of building and maintaining scalable dbt projects in a production setting with Snowflake and Airflow.
• A strong sense of ownership, documentation habits, and attention to data quality, governance and performance.

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